Every approach to altering or analyzing datasets, which inherently involves modifications to the code, is deemed an "experiment". Naturally, we aim to keep track of each experimental iteration. We must regulate the data variants used for evaluating machine learning models. Data versioning signifies cataloging specific points in data development with a unique version label. The significance of this machine learning method is paramount, given the critical nature of reverting to a particular scenario that led to a specific model's evolution.
Why Data Versioning Matters
Data versioning in a database is crucial as it enables quicker data product creation while avoiding errors. Have you ever unintentionally wiped out terabytes of production data? Rather than re-executing a backfill operation that could consume the entire day, it's simpler to retrieve a previous version of a dataset. Need to spot altered entries in a table without a trustworthy last updated column or CDC log? Maintaining multiple data snapshots and seeking inconsistencies should do the trick. In order to hasten their progress, data teams can decrease error costs and illustrate data's evolutionary trajectory, with data model versioning acting as the enabler.
Limitations of Data Versioning
Choosing the right service provider can be challenging due to the prevalent use of cloud software solutions. Data versioning also poses certain data security issues and storage space utilization.
Selecting the Best Provider – If you opt for a versioning service, make sure to pick one that fulfills your organizational needs. Many cloud providers offer diverse features and impose different charges, making it essential to balance your choices for optimized cloud expenditure. Appraise the tools based on factors such as open-source vs proprietary, user-friendly UI, support for popular clouds, storage space, and cost.
- Security Concerns – To protect their reputation, companies need to ensure data security. However, storing more versions of data increases the risk of data leakage or loss, particularly for cloud users who outsource IT activities, thereby having less control over their data. Recognizing and understanding this vulnerability is necessary for shaping an ideal data versioning strategy.
- Storage Problems – Versioning can present issues when there are many large files. Given that a Git repository contains the complete history of each file, frequent alterations result in a repository that takes a long time to clone and uses lots of disk space. One solution is using the Git LFS extension to manage individual files, but it's not the best option as file sizes are capped at 4GB.
Another solution is to manage versions by adding a software version without altering previous content versions. Though suitable for solo projects, this approach can cause chaos in a collaborative environment where everyone can access and possibly alter the data, creating new datasets. Besides, storing large files alongside code is undesirable due to the inevitable decrease in speed. It's not necessary to acquire every dataset used throughout the project's existence, but only the current one. Hence, appropriate data storage is crucial, the primary concept around which all data versioning tools revolve.
Data Versioning Tools
File versioning can be substituted by dedicated tools. You can either develop your own system or opt for outsourced services. Numerous companies, like DVC machine learning and Pachyderm, provide these services. Businesses that require the following functionalities find these versioning solutions more appropriate:
- Accountability: Data version control can pinpoint where and who generated errors.
- Editing: If multiple people work on the data, using a separate tool supports productivity, as file version control doesn't allow real-time cooperative editing.
- Collaboration: When personnel need to work from various locations, using applications rather than data versioning proves more advantageous.